ABSTRACT
Sleep is important for the well-being of school-aged children. Almost all schools in Hyogo prefecture in Japan were closed from April 7 to May 31, 2020, owing to the coronavirus disease 2019 pandemic. The pandemic restrictions resulted in the disruption of the sleep routines of children. The number of children who experienced sleepiness in class after school closure increased. The number of children who visited our hospital 1 year before and after the closure was 208 (11.73 ± 3.24 years of age) and 155 (11.45 ± 3.30 years), respectively. The number of chief complaints of sleep-related symptoms at the first visits showed no significant difference between the two time periods. The percentage of patients who slept during class increased (but not significantly) after the school closure. However, the mean number and duration of sleep episodes during class significantly increased from 0.31 ± 0.76 to 1.04 ± 1.14 episodes/day and from 15.8 ± 38.6 to 45.7 ± 46.9 min/day (each P < 0.001) before and after school closure, respectively. The total number of patients in our hospital with the primary central disorders of hypersomnolence, i.e., narcolepsy, idiopathic hypersomnia, and Kleine-Levin syndrome, and the number of patients with insufficient sleep syndrome after the school closure significantly increased compared with those before closure (P = 0.034 and 0.048, respectively). School closure was associated with an increased incidence of sleeping during class; therefore, maintaining a stable daily routine for children with sleep disorders could have an alleviating effect.
Subject(s)
COVID-19 , Disorders of Excessive Somnolence , Kleine-Levin Syndrome , Narcolepsy , Child , Humans , COVID-19/epidemiology , Sleep , Disorders of Excessive Somnolence/diagnosis , Narcolepsy/diagnosis , Kleine-Levin Syndrome/diagnosisABSTRACT
Previously, we developed a mathematical model via molecular simulation analysis to predict the infectivity of six SARS-CoV-2 variants. In this report, we aimed to predict the relative risk of the recent new variants of SARS-CoV-2 based on our previous research. We subjected Omicron BA.4/5 and BA.2.75 variants of SARS-CoV-2 to the analysis to determine the evolutionary distance of the spike protein gene (S gene) of the variants from the Wuhan variant so as to appreciate the changes in the spike protein. We performed molecular docking simulation analyses of the spike proteins with human angiotensin-converting enzyme 2 (ACE2) to understand the docking affinities of these variants. We then compared the evolutionary distances and the docking affinities of these variants with those of the variants that we had analyzed in our previous research. As a result, BA.2.75 has both the highest docking affinity (ratio per Wuhan variant) and the longest evolutionary distance of the S gene from the Wuhan variant. These results suggest that BA.2.75 infection can spread farther than can infections of preexisting variants.
ABSTRACT
Objectives: Variants of a coronavirus (SARS-CoV-2) have been spreading in a global pandemic. Improved understanding of the infectivity of future new variants is important so that effective countermeasures against them can be quickly undertaken. In our research reported here, we aimed to predict the infectivity of SARS-CoV-2 by using a mathematical model with molecular simulation analysis, and we used phylogenetic analysis to determine the evolutionary distance of the spike protein gene (S gene) of SARS-CoV-2. Methods: We subjected the six variants and the wild type of spike protein and human angiotensin-converting enzyme 2 (ACE2) to molecular docking simulation analyses to understand the binding affinity of spike protein and ACE2. We then utilized regression analysis of the correlation coefficient of the mathematical model and the infectivity of SARS-CoV-2 to predict infectivity. Results: The evolutionary distance of the S gene correlated with the infectivity of SARS-CoV-2 variants. The calculated biding affinity for the mathematical model obtained with results of molecular docking simulation also correlated with the infectivity of SARS-CoV-2 variants. These results suggest that the data from the docking simulation for the receptor binding domain of variant spike proteins and human ACE2 were valuable for prediction of SARS-CoV-2 infectivity. Conclusion: We developed a mathematical model for prediction of SARS-CoV-2 variant infectivity by using binding affinity obtained via molecular docking and the evolutionary distance of the S gene.
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses its S1 spike protein to bind to angiotensin-converting enzyme 2 (ACE2) on human cells in the first step of cell entry. Tryptanthrin, extracted from leaves of the indigo plant, Polygonum tinctorium, using d-limonene (17.3 µg/ml), is considered to inhibit ACE2-mediated cell entry of another type of coronavirus, HCoV-NL63. The current study examined whether this extract could inhibit the binding of the SARS-CoV-2 spike protein to ACE2. Binding was quantified as cell-bound fluorescence intensity in live cell cultures in which canine kidney MDCK cells overexpressing ACE2 were incubated with fluorescein-labeled S1 spike protein. When indigo extract, together with S1 protein, was added at 8,650x and 17,300x dilutions, fluorescence intensity decreased in a dose- and S1 extract-dependent manner, without affecting cell viability. When 4.0-nM tryptanthrin was added instead of the indigo extract, fluorescence intensity also decreased, but to a lesser degree than with indigo extract. Docking simulation analyses revealed that tryptanthrin readily bound to the receptor-binding domain of the S1 protein, and identified 2- and 7-amino acid sequences as the preferred binding sites. The indigo extract appeared to inhibit S1-ACE2 binding at high dilutions, and evidently contained other inhibitory elements as well as tryptanthrin. This extract may be useful for the prevention or treatment of SARS-CoV-2 infection.